1. Field
The present disclosure relates to clinical genomics, and more particularly, to methods and devices for mutation prioritization for personalized therapy.
2. Description of the Related Art
Next generation sequencing (NGS)-based personalized diagnostics hold great potential as a valuable tool for clinical decision making in healthcare. Its market is currently estimated to be 393 million USD and is expected to grow at a fast pace in coming years. The emphasis of personalized diagnostics has been on genetic disorders, especially on cancer. With 1 million cancer cases being diagnosed annually in the US alone and poor response rates (about 25%) to generic treatments, NGS-based diagnostics may have a significant impact on prescribing effective treatment to an individual.
Such personalized diagnostics are based on analysis of a set of mutations obtained by analyzing DNA data of individuals through a NGS analysis pipeline. These mutations, which characterize an individual's disease, help clinicians in tailoring therapy to the individual's disease. Although very promising, several challenges need to be addressed before mutation data becomes useful for personalized therapy. A key issue is to organize often unstructured data such as mutation-disease association or cancer-specific targeted therapy information into a structured format for automated analysis. Systematic organization of relevant information plays a vital role in data-driven approaches that leverage existing knowledge to recommend therapy options to clinicians and researchers.
Existing approaches often focus on therapies and on prioritizing the therapies. Evidence used in these approaches is extracted and curated from similar knowledge sources as used in the present disclosure. This evidence can include, clinical trials and publications supporting the use of a particular therapy, among other sources. In addition, biomarker data can also used. In other approaches, mutations are classified using evidence from sources such as publications into different classes based on the evidence contained in the publication.
Thus, there exists a need for a method that considers a user specified knowledgebase, obtains mutations of a patient, prioritize mutations based on data gathered from the knowledgebase, and assists in deciding treatment options for the patient based on information gathered regarding one or more mutations in question.